Why trustworthy agent evals need per-task isolation, shown live on Tensorlake microVM sandboxes
Catches 53% lie rates in agents using Firecracker microVM isolation.
AI researchers, LLM agent developers
LangSmith · Braintrust · MLflow
Deterministic agent benchmarking with strict validation—unlike SWE-Bench, measures whether agents actually operate.
Claude Skill for agent evals, but LangSmith and Arize already own this.
Manifest-driven agents with eval feedback loops when most harnesses are prompt-only.
Finally forces AI agents to prove their work with real test gates instead of hallucinated confidence.
Terminal-native prompt evals with diff proposals beats web dashboards.
Catches agent failures in Swahili and Chinese that English testing misses.